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dc.contributor.advisorLu, Fletcher
dc.contributor.authorCiolko, Ewelina
dc.date.accessioned2013-07-08T16:47:54Z
dc.date.accessioned2022-03-29T16:53:50Z
dc.date.available2013-07-08T16:47:54Z
dc.date.available2022-03-29T16:53:50Z
dc.date.issued2013-05-01
dc.identifier.urihttps://hdl.handle.net/10155/318
dc.description.abstractSynonyms within SNOMED CT’s structure give meaning to the clinical terminology. The hypothesis in this thesis is that the number of synonyms of a disease within SNOMED CT can be used to predict the number of occurrences of an infectious disease reported on by the World Health Organization (WHO). Using simple Classification and Regression (CART), Bayes theory, and Best Fit trees, prediction algorithms are created based on the number of synonyms in infectious disease terms of SNOMED CT, the number of those diseases world-wide, the region of occurrence of the disease, and the year of occurrence of the disease. The results of experiments predict the number of occurrences of a disease correctly 67% of the time by using Simple Cart method; Bayes and Best Fit Trees each produce the correct number of occurrences 61% of the time.en
dc.description.sponsorshipUniversity of Ontario Institute of Technologyen
dc.language.isoenen
dc.subjectSNOMED CTen
dc.subjectData miningen
dc.subjectWorld Health Organizationen
dc.subjectInfectious diseasesen
dc.subjectSimple CART theoryen
dc.subjectNaive Bayesen
dc.subjectBest Fit Treesen
dc.subjectWorld health statisticsen
dc.titleData mining occurrences of infectious diseases with SNOMED CTen
dc.typeThesisen
dc.degree.levelMaster of Health Sciences (MHSc)en
dc.degree.disciplineHealth Informaticsen


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